Computer Sciences Commons^™

Trustworthiness Of Web Services, Britto N. Arockiasamy

UNF Graduate Theses and Dissertations

Workflow systems orchestrate various business tasks to attain an objective. Web services can be leveraged to handle individual tasks. Before anyone intends to leverage service components, it is imperative and essential to evaluate the trustworthiness of these services. Therefore, choosing a trustworthy service has become an important decision while designing a workflow system. Trustworthiness can be defined as the likelihood of a service functioning as it is intended.

Selection of a service that satisfies business goals involves collecting relevant information such as security mechanisms, reliability, performance and availability. It is important to arrive at total trustworthiness, which incorporates all of …

Sherlock: Microenvironment Sensing For Smartphones, Zheng Yang, Longfei Shangguan, Weixi Gu, Zimu Zhou, Chenshu Wu, Yunhao Liu

Research Collection School Of Computing and Information Systems

Context-awareness is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. Whereas human-centric contexts (e.g., indoor/ outdoor, at home/in office, driving/walking) have been extensively researched, few attempts have studied from phones’ perspective (e.g., on table/sofa, in pocket/bag/hand). We refer to such immediate surroundings as micro-environment, usually several to a dozen of centimeters, around a phone. In this study, we design and implement Sherlock, a micro-environment sensing platform that automatically records sensor hints and characterizes the micro-environment of smartphones. The platform runs as a daemon process on a smartphone and provides finer-grained environment information to upper …

Measuring And Modelling The Thermal Performance Of The Tamar Suspension Bridge Using A Wireless Sensor Network, Nicholas De Battista, James M. W. Brownjohn, Hwee-Pink Tan, Ki Young Koo

Research Collection School Of Computing and Information Systems

A study on the thermal performance of the Tamar Suspension Bridge deck in Plymouth, UK, is presented in this paper. Ambient air, suspension cable, deck and truss temperatures were acquired using a wired sensor system. Deck extension data were acquired using a two-hop wireless sensor network. Empirical models relating the deck extension to various combinations of temperatures were derived and compared. The most accurate model, which used all the four temperature variables, predicted the deck extension with an accuracy of 99.4%. Time delays ranging from 10 to 66 min were identified between the daily cycles of the air temperature and …

Free Market Of Crowdsourcing: Incentive Mechanism Design For Mobile Sensing, Xinglin Zhang, Zheng Yang, Zimu Zhou, Haibin Cai, Lei Chen, Xiang-Yang Li

Research Collection School Of Computing and Information Systems

Off-the-shelf smartphones have boosted large scale participatory sensing applications as they are equipped with various functional sensors, possess powerful computation and communication capabilities, and proliferate at a breathtaking pace. Yet the low participation level of smartphone users due to various resource consumptions, such as time and power, remains a hurdle that prevents the enjoyment brought by sensing applications. Recently, some researchers have done pioneer works in motivating users to contribute their resources by designing incentive mechanisms, which are able to provide certain rewards for participation. However, none of these works considered smartphone users’ nature of opportunistically occurring in the area …

Complexity Of The Soundness Problem Of Workflow Nets, Guan Jun Liu, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Classical workflow nets (WF-nets for short) are an important subclass of Petri nets that are widely used to model and analyze workflow systems. Soundness is a crucial property of workflow systems and guarantees that these systems are deadlock-free and bounded. Aalst et al. proved that the soundness problem is decidable for WF-nets and can be polynomially solvable for free-choice WF-nets. This paper proves that the soundness problem is PSPACE-hard for WF-nets. Furthermore, it is proven that the soundness problem is PSPACE-complete for bounded WF-nets. Based on the above conclusion, it is derived that the soundness problem is also PSPACE-complete for …

Inferring The Untold: Mining Software Engineering Research Publication Networks, Santonu Sarkar, Subhajit Datta

Research Collection School Of Computing and Information Systems

Since the inception of organized research publication in software engineering in 1975, the discipline has gained maturity. This journey has been guided by the synergy of ideas and interactions of individuals. In this paper, we discuss a method for aggregating the corpus of 19,000+ papers and 21,000+ authors across 16 specialized software engineering venues. We focus on the approach of data collection, processing and storage. It can be used to address questions by the software engineering research community. We evaluate three questions: patterns of research topics with time, factors influencing the contribution of individual researchers, and the interaction among the …

A Hybrid Approach To Music Recommendation: Exploiting Collaborative Music Tags And Acoustic Features, Jaime C. Kaufman

UNF Graduate Theses and Dissertations

Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering.

Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and …

Application Of Cellular Neural Networks And Naive Bayes Classifier In Agriculture, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen

Research outputs 2014 to 2021

This article describes the use of Cellular Neural Networks (a class of Ordinary Differential Equation (ODE)), Fourier Descriptors (FD) and NaiveBayes Classifier (NBC) for automatic identification of images of plant leaves. The novelty of this article is seen in the use of CNN for image segmentation and a combination FDs with NBC. The main advantage of the segmentation method is the computation speed compared with other edge operators such as canny, sobel, Laplacian of Gaussian (LoG). The results herein show the potential of the methods in this paper for examining different agricultural images and distinguishing between different crops and weeds …

Mobile Applications For Indian Agriculture Sector: A Case Study, Pratik Shah, Niketa Gandhi, Leisa Armstrong

Research outputs 2014 to 2021

Government, private agencies and the general public are often interested in the decisions made by the Indian farmers as they have large influences beyond the farm boundary. Over many years, the process of adoption of new technologies and policies in the Indian agricultural sector has received considerable academic attention highlighting the role of many social, financial and other influences on their decision making. The Indian government and other development agencies promote income generating projects as a way of encouraging growth through increased agricultural production and the protection of the natural resource base. The impact of new technology to economic growth …

Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig

Research outputs 2014 to 2021

Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used …

Using Software-Based Decision Procedures To Control Instruction-Level Execution, William B. Kimball

AFIT Patents

An apparatus, method and program product are provided for securing a computer system. A digital signature of an application is checked, which is loaded into a memory of the computer system configured to contain memory pages. In response to finding a valid digital signature, memory pages containing instructions of the application are set as executable and memory pages other than those containing instructions of the application are set as non-executable. Instructions in executable memory pages are executed. Instructions in non-executable memory pages are prevented from being executed. A page fault is generated in response to an attempt to execute an …

Forensic Investigation Of Mysql Database Management System, Andrew C. Lawrence

Computer Science and Computer Engineering Undergraduate Honors Theses

For various reasons, circumstances might arise in which an investigator, enlisting the help of a system administrator, needs access to an instance of MySQL that is password protected by an individual system user. If this password is unknown and the user is uncooperative or unavailable, alternative means must be utilized to gain access to the data stored within the program. Two main approaches will be explored, each with its pros and cons. In one case, the password can be bypassed entirely, granting the investigator unfettered access to the program data. The second method allows a narrower look at only some …

Uos : A Resource Rerouting Middleware For Ubiquitous Games, Fabricio N. Buzeto, Miriam A M Capretz, Carla D. Castanho, Ricardo P. Jacobi

Electrical and Computer Engineering Publications

Ubiquitous computing (ubicomp) relies on the computation distributed over the environment to simplify the tasks performed by its users. A smart space is an instance of a ubiquitous environment, composed of a dynamic and heterogeneous set of devices that interact to support the execution of distributed smart applications. In this context, mobile devices provide new resources when they join the environment, which disappear when they leave it. This introduces the challenge of self-adaptation, in which smart applications may either include new resources as they become available or replace them when they become unavailable. Ubiquitous games combine ubicomp and computer game …

Reconstructing Point Clouds Of Mid-Size Objects, Spencer Woodworth

Computer Science and Software Engineering

This project explores the use of an inexpensive 3D camera for the acquisition and reconstruction of mid-size objects. The disparity of objects between stereo image pairs are used to calculate depth and generate a depth map. The depth map is used to generate a point cloud representation of the object from a single view. Finally, point clouds are generated from several views of an object and then aligned and merged into a seamless 360-degree point cloud.

Can Clustering Improve Requirements Traceability? A Tracelab-Enabled Study, Brett Taylor Armstrong

Master's Theses

Software permeates every aspect of our modern lives. In many applications, such in the software for airplane flight controls, or nuclear power control systems software failures can have catastrophic consequences. As we place so much trust in software, how can we know if it is trustworthy? Through software assurance, we can attempt to quantify just that.

Building complex, high assurance software is no simple task. The difficult information landscape of a software engineering project can make verification and validation, the process by which the assurance of a software is assessed, very difficult. In order to manage the inevitable information overload …

Fundamental Limits On End-To-End Throughput Of Network Coding In Multi-Rate And Multicast Wireless Networks, Luiz Felipe Viera, Mario Gerla, Archan Misra

Research Collection School Of Computing and Information Systems

This paper investigates the interaction between network coding and link-layer transmission rate diversity in multi-hop wireless networks. By appropriately mixing data packets at intermediate nodes, network coding allows a single multicast flow to achieve higher throughput to a set of receivers. Broadcast applications can also exploit link-layer rate diversity, whereby individual nodes can transmit at faster rates at the expense of corresponding smaller coverage area. We first demonstrate how combining rate-diversity with network coding can provide a larger capacity for data dissemination of a single multicast flow, and how consideration of rate diversity is critical for maximizing system throughput. Next …

Dense Image Correspondence Under Large Appearance Variations, Linlin Liu, Kok-Lim Low, Wen-Yan Lin

Research Collection School Of Computing and Information Systems

This paper addresses the difficult problem of finding dense correspondence across images with large appearance variations. Our method uses multiple feature samples at each pixel to deal with the appearance variations based on our observation that pre-defined single feature sample provides poor results in nearest neighbor matching. We apply the idea in a flow-based matching framework and utilize the best feature sample for each pixel to determine the flow field. We propose a novel energy function and use dual-layer loopy belief propagation to minimize it where the correspondence, the feature scale and rotation parameters are solved simultaneously. Our method is …

Factors Influencing Research Contributions And Researcher Interactions In Software Engineering: An Empirical Study, Subhajit Datta, A. S. M. Sajeev, Santonu Sarkar, Nishant Kumar

Research Collection School Of Computing and Information Systems

Research into software engineering (SE) education is largely concentrated on teaching and learning issues in coursework programs. This paper, in contrast, provides a meta analysis of research publications in software engineering to help with research education in SE. Studying publication patterns in a discipline will assist research students and supervisors gain a deeper understanding of how successful research has occurred in the discipline. We present results from a large scale empirical study covering over three and a half decades of software engineering research publications. We identify how different factors of publishing relate to the number of papers published as well …

Towards A Hybrid Framework For Detecting Input Manipulation Vulnerabilities, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar, Bindu Madhavi Padmanabhuni

Research Collection School Of Computing and Information Systems

Input manipulation vulnerabilities such as SQL Injection, Cross-site scripting, Buffer Overflow vulnerabilities are highly prevalent and pose critical security risks. As a result, many methods have been proposed to apply static analysis, dynamic analysis or a combination of them, to detect such security vulnerabilities. Most of the existing methods classify vulnerabilities into safe and unsafe. They have both false-positive and false-negative cases. In general, security vulnerability can be classified into three cases: (1) provable safe, (2) provable unsafe, (3) unsure. In this paper, we propose a hybrid framework-Detecting Input Manipulation Vulnerabilities (DIMV), to verify the adequacy of security vulnerability defenses …

Exposing And Mitigating Privacy Loss In Crowdsourced Survey Platforms, Thivya Kandappu, Vijay Sivaraman, Arik Friedman, Roksana Borell

Research Collection School Of Computing and Information Systems

Crowdsourcing platforms such as Amazon Mechanical Turk and Google Consumer Surveys can profile users based on their inputs to online surveys. In this work we first demonstrate how easily user privacy can be compromised by collating information from multiple surveys. We then propose, develop, and evaluate a crowdsourcing survey platform called Loki that allows users to control their privacy loss via atsource obfuscation.

Object Detection Using Contrast Enhancement And Dynamic Noise Reduction, Justin Lee Baker

UNLV Theses, Dissertations, Professional Papers, and Capstones

Edge detection is one of the most important steps a computer must perform to gain understanding of an object in a digital image either from disk or from video feed. Edge detection allows for the computer to describe the shape of the objects in an image and create a pixel boundary defining what is considered part of an object, and what is not. Cannys edge detection algorithm is one of the most robust and accurate of these edge detection algorithms. However, as with many algorithms in image processing, there are many cases where the algorithm does not perform as well …

Mapping The Invisible: A Framework For Tracking Covid-19 Spread Among College Students With Google Location Data, Prajindra Sankar Krishnan, Chai Phing Chen, Gamal Alkawsi, Sieh Kiong Tiong, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

The COVID-19 pandemic and the implementation of social distancing policies have rapidly changed people's visiting patterns, as reflected in mobility data that tracks mobility traffic using location trackers on cell phones. However, the frequency and duration of concurrent occupancy at specific locations govern the transmission rather than the number of customers visiting. Therefore, understanding how people interact in different locations is crucial to target policies, inform contact tracing, and prevention strategies. This study proposes an efficient way to reduce the spread of the virus among on-campus university students by developing a self-developed Google History Location Extractor and Indicator software based …

From Rssi To Csi: Indoor Localization Via Channel Response, Zheng Yang, Zimu Zhou, Yunhao Liu

Research Collection School Of Computing and Information Systems

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics.

Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall

Research Collection School Of Computing and Information Systems

Attempts have been made to understand driver development in terms of code clones. In this paper, we propose an alternate view, based on the metaphor of a gene. Guided by this metaphor, we study the structure of Linux 3.10 ethernet platform driver probe functions.

Challenges And Opportunities In Taxi Fleet Anomaly Detection, Rijurekha Sen, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

To enhance fleet operation and management, logistics companies instrument their vehicles with GPS receivers and network connectivity to servers. Mobility traces from such large fleets provide significant information on commuter travel patterns, traffic congestion and road anomalies, and hence several researchers have mined such datasets to gain useful urban insights. These logistics companies, however, incur significant cost in deploying and maintaining their vast network of instrumented vehicles. Thus research problems, that are not only of interest to urban planners, but to the logistics companies themselves are important to attract and engage these companies for collaborative data analysis. In this paper, …

A Scalable Approach For Malware Detection Through Bounded Feature Space Behavior Modeling, Mahinthan Chandramohan, Hee Beng Kuan Tan, Lionel C Briand, Lwin Khin Shar, Bindu Madhavi Padmanabhuni

Research Collection School Of Computing and Information Systems

In recent years, malware (malicious software) has greatly evolved and has become very sophisticated. The evolution of malware makes it difficult to detect using traditional signature-based malware detectors. Thus, researchers have proposed various behavior-based malware detection techniques to mitigate this problem. However, there are still serious shortcomings, related to scalability and computational complexity, in existing malware behavior modeling techniques. This raises questions about the practical applicability of these techniques. This paper proposes and evaluates a bounded feature space behavior modeling (BOFM) framework for scalable malware detection. BOFM models the interactions between software (which can be malware or benign) and security-critical …

Automatically Partition Software Into Least Privilege Components Using Dynamic Data Dependency Analysis, Yongzheng Wu, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

The principle of least privilege requires that software components should be granted only necessary privileges, so that compromising one component does not lead to compromising others. However, writing privilege separated software is difficult and as a result, a large number of software is monolithic, i.e., it runs as a whole without separation. Manually rewriting monolithic software into privilege separated software requires significant effort and can be error prone. We propose ProgramCutter, a novel approach to automatically partitioning monolithic software using dynamic data dependency analysis. ProgramCutter works by constructing a data dependency graph whose nodes are functions and edges are data …

Social-Loc: Improving Indoor Localization With Social Sensing, Jung-Hyun Jun, Yu Gu, Long Cheng, Banghui Lu, Jun Sun, Ting Zhu, Jianwei Niu

Research Collection School Of Computing and Information Systems

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the …

Tzuyu: Learning Stateful Typestates, Hao Xiao, Jun Sun, Yang Liu, Shang-Wei Lin, Chengnian Sun

Research Collection School Of Computing and Information Systems

Behavioral models are useful for various software engineering tasks. They are, however, often missing in practice. Thus, specification mining was proposed to tackle this problem. Existing work either focuses on learning simple behavioral models such as finite-state automata, or relies on techniques (e.g., symbolic execution) to infer finite-state machines equipped with data states, referred to as stateful typestates. The former is often inadequate as finite-state automata lack expressiveness in capturing behaviors of data-rich programs, whereas the latter is often not scalable. In this work, we propose a fully automated approach to learn stateful typestates by extending the classic active learning …

Mining Branching-Time Scenarios, Dirk Fahland, David Lo, Shahar Maoz

Research Collection School Of Computing and Information Systems

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows …